Profit drives many corporate decisions. Yet, the long-term profitability of many decisions and transactions is hard to measure and is complicated by uncertainty (e.g., future revenues and cost) and complexity. Profit is uncertain because “black-and-white” business decisions are often made with “gray” information, such as personal expertise and gut-level assumptions about the future. Currently, there is little or no infrastructure to measure and manage such instinct-driven decisions. Furthermore, most business analytics rely at least primarily on historical data to predict future outcomes. But the past is often a poor predictor of the future. Consequently, long-term profits associated with business decisions are difficult to evaluate until after commitments have been made. Companies need a way to evaluate the impact of uncertainty.
One goal of profit optimization (PO) is to provide decision-makers with information that should guide them to more profitable decisions. In most cases, existing solutions are data-centric, focusing on all the data in transactional database systems such as enterprise resource planning systems and customer relationship management systems. Consequently, extensive integration projects are required to link the transactional systems' databases into a common platform for analysis and reporting. Once integrated, the systems produce a deluge of data about historical trends, in the form of analytical reports. While analysis of past transactions does support better decisions, it is only a part of the decision-making process.
Complexity compounds the problem. Many transactions include complex bundles of products, services, financing terms, and a host of other factors. Since many organizations rank the profitability of opportunities based on departmental objectives or local expertise, they can overlook how differing priorities impact the overall profitability of a customer relationship, or how individual transactions impact portfolios of opportunities. As the demand for customized transactions increases, so too does the complexity of managing those transactions to a profitable end. Complexity is further compounded when the entire portfolio is considered.
Most data-centric analytic models focus on interpreting the trends and key drivers found within all available company data. Consequently, intense and often costly integration is required to link these transactional databases for traditional reporting means. While better than nothing, these forecasting approaches are predicated on historical, empirical data that is limited by its lack of relation to current market or company conditions. Decision makers are therefore left to predict the future based on events of the past, externally considering intangibles such as personal expertise and instinct to effect profitable decision making. In short, the decision-making occurs outside the system. There is no way to track the effectiveness of decisions or to retain the methods used to reach profitable decisions.
Under existing schemes, after reviewing reports, decision-makers usually include information they know, but that is not stored in the transactional databases. They interpret the combined data within the context of their personal expertise and judgment to form opinions about the future. They then apply their own functionally biased reasoning to reach a decision all outside the system. The relationship of the decision to the analysis is not tracked or measured. It is difficult to directly link the results of the analysis with the profitability of the related business decision. Most existing systems attempt to provide PO software based on a data-centric approach, because they have evolved from database systems.
These and other drawbacks exist.